By converting retrieved text into executable Prolog rules, you get verifiable reasoning traces linked to sources and automatic detection of missing information—achieving 43% higher accuracy than standard RAG on a benchmark.
NeSy-RAG combines neural language models with symbolic logic (Prolog) to make question-answering systems more transparent and verifiable. Instead of treating retrieved information as black-box text, it converts document chunks into logical rules that can be executed step-by-step, showing exactly which sources support each conclusion and detecting when user context is missing.